Class imbalance can make software defect predictors appear successful while missing defective modules. This study compared no correction, random oversampling, random undersampling, SMOTE, ADASYN, and class-weighted learning across logistic regression, decision tree, random forest, support vector machine, and neural network classifiers. KC1 and PC1 NASA/PROMISE datasets (3,218 modules; 403 defective) were evaluated by stratified five-fold cross-validation. Imputation, scaling, and correction were fitted only within training folds. Precision, sensitivity, specificity, F1-score, balanced accuracy, ROC-AUC, PR-AUC, and confusion matrices were reported. Across classifiers, baseline balanced accuracy was 0.580; corrected means ranged from 0.696 to 0.720. Correction increased sensitivity but generally reduced precision and specificity. ROS achieved the highest mean F1-score (0.388), while SMOTE achieved the highest mean PR-AUC (0.378). A Friedman comparison indicated heterogeneity among techniques, followed by Holm-adjusted paired Wilcoxon tests. No approach dominated every classifier or metric. Leakage-safe correction and multi-metric assessment are essential; accuracy alone is unsuitable for selecting defect predictors.